Evidence map›Paper›PMID 42194331›Full record

ArticleBioengineering (Basel, Switzerland)2026

Quantifying the Contribution of Bone Morphology to Implant Selection in Shoulder Arthroplasty Using CT-Based Deep Learning.

Andrea Moglia, Luca Marsilio, Matteo Rossi, Alfonso Manzotti, Luca Mainardi, Pietro Cerveri

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Andrea MogliaDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.ORCID 0000-0002-3365-580X
Luca MarsilioDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.ORCID 0009-0001-9738-0438
Matteo RossiDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.ORCID 0000-0003-2519-0720
Alfonso ManzottiHospital ASST FBF-Sacco, 20154 Milan, Italy.ORCID 0000-0003-1791-6800
Luca MainardiDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.ORCID 0000-0002-6276-6314
Pietro CerveriDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy.ORCID 0000-0003-3995-8673

Funding

Ministry of Universities and Research P.E. PE00000013
6 · The paper itself

Abstract

We investigated whether bone morphology alone can inform implant selection in shoulder arthroplasty using a hypothesis-driven deep learning framework applied to preoperative computed tomography (CT) scans. The proposed approach extends a previously validated segmentation and pathology-staging pipeline by introducing implant-type prediction and a controlled human-AI comparison. The workflow combines CEL-UNet for 3D bone segmentation with ArthroNet

Indexed as

anatomical/reverse implantclinical translationdeep learningshoulder arthroplasty planning

Identifiers

PMID42194331
PMCPMC13203169

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.